CN111164012A - Maintenance device control system, maintenance device control method, and program - Google Patents
Maintenance device control system, maintenance device control method, and program Download PDFInfo
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- CN111164012A CN111164012A CN201780095476.XA CN201780095476A CN111164012A CN 111164012 A CN111164012 A CN 111164012A CN 201780095476 A CN201780095476 A CN 201780095476A CN 111164012 A CN111164012 A CN 111164012A
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- 238000012423 maintenance Methods 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims description 15
- 230000002950 deficient Effects 0.000 claims abstract description 33
- 230000007547 defect Effects 0.000 claims abstract description 26
- 238000003384 imaging method Methods 0.000 description 12
- 230000000694 effects Effects 0.000 description 5
- 238000010801 machine learning Methods 0.000 description 5
- 238000010191 image analysis Methods 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
- B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D45/00—Aircraft indicators or protectors not otherwise provided for
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/006—Indicating maintenance
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D45/00—Aircraft indicators or protectors not otherwise provided for
- B64D2045/0085—Devices for aircraft health monitoring, e.g. monitoring flutter or vibration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
- B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Aviation & Aerospace Engineering (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Signal Processing (AREA)
- Analytical Chemistry (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Image Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
The problem to be solved by the present invention is to analyze an image captured by a camera of an unmanned aerial vehicle to determine a defect of a subject, and to control a maintenance device so as to maintain the subject determined to be defective at a precise position. As a solution, the present invention provides a maintenance equipment control system that controls maintenance equipment to perform maintenance, the maintenance equipment control system including: an image acquisition unit that acquires an image photographed by the unmanned aerial vehicle; a failure determination unit configured to analyze the image to determine a failure of the subject; a position estimation unit that estimates a position of the subject determined to be defective; and a device control unit that controls the maintenance device so as to maintain a defect in the object located at the estimated position.
Description
Technical Field
The present invention relates to a maintenance device control system, a maintenance device control method, and a program for controlling and maintaining a maintenance device.
Background
In recent years, from the viewpoint of efficiency, etc., a technique of maintenance using an unmanned aerial vehicle is advancing. For example, there has been provided an in-facility checkup system using an unmanned aerial vehicle, which can perform a check of equipment provided in a facility even if an operator does not go to the site (patent document 1).
Documents of the prior art
Patent document
Patent document 1: japanese patent laid-open publication No. 2017-154577
Disclosure of Invention
Problems to be solved by the invention
However, the device of patent document 1 has a problem that a defect of the device to be detected is detected, but maintenance cannot be performed at a precise position. That is, it is not possible to extract only a defective portion and perform maintenance, but to perform overall maintenance on the entire object.
In view of the above-described problems, it is an object of the present invention to provide a maintenance device control system, a maintenance device control method, and a program that analyze an image captured by a camera of an unmanned aerial vehicle, determine a defect of a subject, and control a maintenance device so as to maintain a precise position (pinpoint) of the subject determined to be defective.
Means for solving the problems
In the present invention, the following solution is provided.
A first aspect of the present invention provides a maintenance equipment control system that controls maintenance equipment to perform maintenance, the maintenance equipment control system including: an image acquisition unit that acquires an image photographed by the unmanned aerial vehicle; a failure determination unit configured to analyze the image to determine a failure of the subject; a position estimation unit that estimates a position of the subject determined to be defective; and a device control unit that controls the maintenance device so as to maintain a defect in the object located at the estimated position.
A first aspect of the present invention provides a maintenance equipment control method for controlling a maintenance equipment to perform maintenance, the method including: an image acquisition step of acquiring an image photographed by the unmanned aerial vehicle; a defect determination step of analyzing the image to determine a defect of the subject; a position estimation step of estimating a position of the subject determined to be defective; and a device control step of controlling the maintenance device so as to maintain a defect in the object located at the estimated position.
The invention of the first feature provides a program for executing: an image acquisition step of acquiring an image photographed by the unmanned aerial vehicle; a defect determination step of analyzing the image to determine a defect of the subject; a position estimation step of estimating a position of the subject determined to be defective; and a device control step of controlling the maintenance device so as to maintain a defect in the object located at the estimated position.
Effects of the invention
The maintenance can be performed on the defective object at the accurate position.
Drawings
Fig. 1 is a schematic diagram of a maintenance equipment control system.
Fig. 2 is an example of a track determined to be defective.
Detailed Description
The best mode for carrying out the present invention will be described below. This is merely an example, and the technical scope of the present invention is not limited thereto.
The maintenance equipment control system of the present invention is a system for controlling maintenance equipment to perform maintenance on an object.
An outline of a preferred embodiment of the present invention will be described with reference to fig. 1. Fig. 1 is a schematic diagram of a maintenance equipment control system according to a preferred embodiment of the present invention.
As shown in fig. 1, the maintenance equipment control system includes an image acquisition unit, a failure determination unit, a position estimation unit, and a maintenance equipment control unit, which are realized by a control unit reading a predetermined program. Although not shown, the unmanned aerial vehicle control unit may also be provided similarly. These may be of the application type, cloud (cloud) type or others. Each of the above units may be implemented by a single computer, or may be implemented by two or more computers (for example, in the case of a server and a terminal).
The image acquisition unit acquires an image captured by a camera of the unmanned aerial vehicle. The image may be a moving image or a still image. The camera may be a camera provided in the unmanned aerial vehicle, and may be a digital camera or a camera of a smart phone. For maintenance in real time, real time images are preferred.
The determination unit analyzes the image to determine a defect of the subject. The accuracy of image analysis can be improved by machine learning. For example, machine learning is performed using the past image as teacher data. For example, as shown in fig. 2, image analysis may be performed to determine a defect in the track. Of course, the determination may be made not only for the track but also for defects in buildings, roads, terminals, and the like. For example, in comparison with a case where a field operator determines a failure while walking near a track, the failure is determined by analyzing an image captured by an unmanned aerial vehicle, and significant efficiency is achieved.
The failure determination unit may analyze the image and determine that the subject is defective when the size satisfies a predetermined condition. For example, when the size of the rail is larger or smaller than a reference value, there is a possibility of derailment and a danger. In this case, it is determined that the track is defective.
The failure determination unit may analyze the image and determine that the subject is defective when the color satisfies a predetermined condition. For example, when the color of the track is lighter or darker than a reference value, there is a possibility of derailment and a danger when the track is broken due to rusting. In this case, it is determined that the track is defective.
The failure determination unit may analyze the image and determine that the subject is defective when the shape satisfies a predetermined condition. For example, when the size of the rail is larger or smaller than a reference value, there is a possibility of derailment and a danger. In this case, it is determined that the track is defective.
A position estimation unit estimates a position of the subject determined to be defective. The position of the subject determined to be a defective subject can be estimated from the GPS provided in the unmanned aerial vehicle, or the imaging height, the imaging angle, and the imaging direction on the unmanned aerial vehicle. For example, in the case of the imaging height H and the imaging angle θ, the position of the object determined to be a poor image formation is a latitude longitude in which Htan θ is added to the latitude longitude of the GPS in consideration of the imaging direction.
The maintenance device control unit controls the maintenance device so as to maintain a defect in the object located at the estimated position. For example, a maintenance device that controls a track so as to maintain the track located at the estimated position. For example, a maintenance-specific drone may be controlled in a manner to maintain a track at the presumed location. The unmanned aerial vehicle special for shooting and the unmanned aerial vehicle special for maintenance are separately used, so that efficient maintenance can be performed.
The drone control unit controls the drone to fly to the estimated position again and to shoot again. For example, since there is no possibility that a poor determination is misjudged, the unmanned aerial vehicle is flown again and photographed again in order to take care of the (care) misjudgment. By analyzing the re-captured image, a double check (check) for determining a failure can be performed.
[ description of operation ]
Next, a maintenance device control method will be described. A method for controlling a maintenance device according to the present invention is a method for controlling a maintenance device to maintain a defective subject.
The maintenance equipment control method includes an image acquisition step, a failure determination step, a position estimation step, and a maintenance equipment control step. In addition, unmanned aerial vehicle control steps can be further provided.
In the image acquisition step, an image captured by a camera of the drone is acquired. The image may be a moving image or a still image. The camera may be a camera provided in the unmanned aerial vehicle, and may be a digital camera or a camera of a smart phone. For maintenance in real time, real time images are preferred.
In the determination step, the image is analyzed to determine a defect of the subject. The accuracy of image analysis can be improved by machine learning. For example, machine learning is performed using the past image as teacher data. For example, as shown in fig. 2, image analysis may be performed to determine a defect in the track. Of course, the determination may be made not only for the track but also for defects in buildings, roads, terminals, and the like. For example, in comparison with a case where a field operator determines a failure while walking near a track, the failure is determined by analyzing an image captured by an unmanned aerial vehicle, and significant efficiency is achieved.
In the failure determination step, the image may be analyzed, and when the size satisfies a predetermined condition, it may be determined that the subject is defective. For example, when the size of the rail is larger or smaller than a reference value, there is a possibility of derailment and a danger. In this case, it is determined that the track is defective.
In the failure determination step, the image may be analyzed, and when the color satisfies a predetermined condition, it may be determined that the subject is defective. For example, when the color of the track is lighter or darker than a reference value, there is a possibility of derailment and a danger when the track is broken due to rusting. In this case, it is determined that the track is defective.
In the failure determination step, the image may be analyzed, and when the shape satisfies a predetermined condition, it may be determined that the subject is defective. For example, when the size of the rail is larger or smaller than a reference value, there is a possibility of derailment and a danger. In this case, it is determined that the track is defective.
In the position estimation step, the position of the subject determined to be defective is estimated. The position of the subject determined to be a defective subject can be estimated from the GPS provided in the unmanned aerial vehicle, or the imaging height, the imaging angle, and the imaging direction on the unmanned aerial vehicle. For example, in the case of the imaging height H and the imaging angle θ, the position of the object determined to be a poor image formation is a latitude longitude in which Htan θ is added to the latitude longitude of the GPS in consideration of the imaging direction.
In the maintenance device control step, the maintenance device is controlled so as to maintain a defect in the object located at the estimated position. For example, a maintenance device that controls a track so as to maintain the track located at the estimated position. For example, a maintenance-specific drone may be controlled in a manner to maintain a track at the presumed location. The unmanned aerial vehicle special for shooting and the unmanned aerial vehicle special for maintenance are separately used, so that efficient maintenance can be performed.
In the drone control step, the drone is controlled so as to fly to the estimated position again and shoot again. For example, since there is no possibility that a determination as a failure is misjudged, the unmanned aerial vehicle is flown again and photographed again in order to take care of the misjudgment. By analyzing the re-captured image, a double check for a failure determination can be performed.
The above-described means and functions are realized by reading and executing a predetermined program by a computer (including a CPU, an information processing apparatus, and various terminals). The program may be an application installed in a computer, may be provided in a SaaS (Software as a Service) system provided from the computer via a network, or may be provided in a computer-readable recording medium such as a flexible disk, a CD (CD-ROM, etc.), a DVD (DVD-ROM, DVD-RAM, etc.), or the like. In this case, the computer reads the program from the recording medium, and transmits and stores the program to the internal storage device or the external storage device to execute the program. The program may be recorded in a storage device (recording medium) such as a magnetic disk, an optical disk, or an opto-magnetic disk in advance, and supplied from the storage device to the computer via a communication line.
As a specific algorithm of the machine learning, a nearest neighbor method, a naive bayes method, a decision tree, a support vector machine, reinforcement learning, and the like can be used. Further, Deep learning (Deep learning) may be performed in which a neural network generates a feature amount for learning by itself.
While the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments. The effects described in the embodiments of the present invention are merely the most preferable effects according to the present invention, and the effects according to the present invention are not limited to the effects described in the embodiments of the present invention.
Claims (8)
1. A maintenance equipment control system that controls maintenance equipment to perform maintenance, the maintenance equipment control system comprising:
an image acquisition unit that acquires an image photographed by the unmanned aerial vehicle;
a failure determination unit configured to analyze the image to determine a failure of the subject;
a position estimation unit that estimates a position of the subject determined to be defective; and
and a device control unit configured to control the maintenance device so as to maintain a defect in the object located at the estimated position.
2. The maintenance equipment control system according to claim 1,
the failure determination means analyzes the image and determines that the subject is defective when the size satisfies a predetermined condition.
3. The maintenance equipment control system according to claim 1,
the failure determination means analyzes the image and determines that the subject is defective when the color satisfies a predetermined condition.
4. The maintenance equipment control system according to claim 1,
the failure determination means analyzes the image and determines that the subject is defective when the shape satisfies a predetermined condition.
5. The maintenance equipment control system according to claim 1,
the position estimation unit estimates the position of the subject determined as being defective, based on the GPS, the shooting height, the shooting angle, and the shooting orientation of the unmanned aerial vehicle.
6. The maintenance equipment control system according to claim 1, comprising:
and an unmanned aerial vehicle control unit for controlling the unmanned aerial vehicle to fly to the estimated position again and shoot again.
7. A maintenance device control method for controlling a maintenance device to perform maintenance, the method comprising:
an image acquisition step of acquiring an image photographed by the unmanned aerial vehicle;
a defect determination step of analyzing the image to determine a defect of the subject;
a position estimation step of estimating a position of the subject determined to be defective; and
and a device control step of controlling the maintenance device so as to maintain a defect in the object located at the estimated position.
8. A program for causing a computer to execute:
an image acquisition step of acquiring an image photographed by the unmanned aerial vehicle;
a defect determination step of analyzing the image to determine a defect of the subject;
a position estimation step of estimating a position of the subject determined to be defective; and
and a device control step of controlling the maintenance device so as to maintain a defect in the object located at the estimated position.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/JP2017/035307 WO2019064456A1 (en) | 2017-09-28 | 2017-09-28 | Maintenance device control system, maintenance device control method, and program |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111164012A true CN111164012A (en) | 2020-05-15 |
Family
ID=65901422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201780095476.XA Withdrawn CN111164012A (en) | 2017-09-28 | 2017-09-28 | Maintenance device control system, maintenance device control method, and program |
Country Status (4)
| Country | Link |
|---|---|
| US (1) | US20200364954A1 (en) |
| JP (1) | JPWO2019064456A1 (en) |
| CN (1) | CN111164012A (en) |
| WO (1) | WO2019064456A1 (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005348463A (en) * | 2004-05-31 | 2005-12-15 | Hitachi Ltd | Method and equipment for conveying movable member |
| CN106062510A (en) * | 2014-04-25 | 2016-10-26 | 索尼公司 | Information processing device, information processing method, and computer program |
| CN106741890A (en) * | 2016-11-28 | 2017-05-31 | 北京交通大学 | A kind of high-speed railway safety detecting system based on the dual-purpose unmanned plane of empty rail |
| CN106954042A (en) * | 2017-03-13 | 2017-07-14 | 兰州交通大学 | A kind of unmanned plane rail track inspection device, system and method |
| US20170206648A1 (en) * | 2016-01-20 | 2017-07-20 | Ez3D, Llc | System and method for structural inspection and construction estimation using an unmanned aerial vehicle |
| US20170270650A1 (en) * | 2016-03-17 | 2017-09-21 | Conduent Business Services, Llc | Image analysis system for property damage assessment and verification |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6486024B2 (en) * | 2014-07-02 | 2019-03-20 | 三菱重工業株式会社 | Indoor monitoring system and method for structure |
| JP6784261B2 (en) * | 2015-10-07 | 2020-11-11 | 日本電気株式会社 | Information processing equipment, image processing system, image processing method and program |
-
2017
- 2017-09-28 JP JP2019545513A patent/JPWO2019064456A1/en active Pending
- 2017-09-28 WO PCT/JP2017/035307 patent/WO2019064456A1/en active Application Filing
- 2017-09-28 US US16/651,732 patent/US20200364954A1/en not_active Abandoned
- 2017-09-28 CN CN201780095476.XA patent/CN111164012A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005348463A (en) * | 2004-05-31 | 2005-12-15 | Hitachi Ltd | Method and equipment for conveying movable member |
| CN106062510A (en) * | 2014-04-25 | 2016-10-26 | 索尼公司 | Information processing device, information processing method, and computer program |
| US20170206648A1 (en) * | 2016-01-20 | 2017-07-20 | Ez3D, Llc | System and method for structural inspection and construction estimation using an unmanned aerial vehicle |
| US20170270650A1 (en) * | 2016-03-17 | 2017-09-21 | Conduent Business Services, Llc | Image analysis system for property damage assessment and verification |
| CN106741890A (en) * | 2016-11-28 | 2017-05-31 | 北京交通大学 | A kind of high-speed railway safety detecting system based on the dual-purpose unmanned plane of empty rail |
| CN106954042A (en) * | 2017-03-13 | 2017-07-14 | 兰州交通大学 | A kind of unmanned plane rail track inspection device, system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| JPWO2019064456A1 (en) | 2020-11-19 |
| US20200364954A1 (en) | 2020-11-19 |
| WO2019064456A1 (en) | 2019-04-04 |
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